14 papers · ranked by Valyu relevance
Oscar Esteban, Christopher J. Markiewicz, Ross W. Blair, Craig A. Moodie + 12 more
Preprocessing of functional MRI (fMRI) involves numerous steps to clean and standardize data before statistical analysis. Generally, researchers create ad hoc preprocessing workflows for each new dataset, building upon a large inventory of tools available for each step. The complexity of these workflows has snowballed…
Shifu Chen, Yanqing Zhou, Yaru Chen, Jia Gu
Quality control (QC) and preprocessing of FASTQ files are necessary steps to provide clean data for downstream analysis. Traditionally, for each operation, such as QC, adapter trimming and quality filtering, a different tool is used. These tools are usually not fast enough since they are mostly developed in high-level…
Mert Demirarslan, Aslı Suner
In disease diagnosis classification, ensemble learning algorithms enable strong and successful models by training more than one learning function simultaneously. This study aimed to eliminate the irrelevant variable problem with the proposed new feature selection method and compare the ensemble learning algorithms’…
Greg Finak, Bryan T. Mayer, William Fulp, Paul Obrecht + 4 more
A central tenet of reproducible research is that scientific results are published along with the underlying data and software code necessary to reproduce and verify the findings. A host of tools and software have been released that facilitate such work-flows and scientific journals have increasingly demanded that code…
Nadine S. J. Jacobsen, Daniel Kristanto, Suong Welp, Yusuf Cosku Inceler + 1 more
Preprocessing is necessary to extract meaningful results from electroencephalography (EEG) data. With many possible preprocessing choices, their impact on outcomes is fundamental. While previous studies have explored the effects of preprocessing on stationary EEG data, this research delves into mobile EEG, where…
Andreas Pedroni, Amirreza Bahreini, Nicolas Langer
Electroencephalography (EEG) recordings have been rarely included in large-scale studies. This is arguably not due to a lack of information that lies in EEG recordings but mainly on account of methodological issues. In many cases, particularly in clinical, pediatric and aging populations, the EEG has a high degree of…
Ranjan Debnath, George A. Buzzell, Santiago Morales, Maureen E. Bowers + 2 more
Compared to adult EEG, EEG signals recorded from pediatric populations have shorter recording periods and contain more artifact contamination. Therefore, pediatric EEG data necessitate specific preprocessing approaches in order to remove environmental noise and physiological artifacts without losing large amounts of…
M. Zanin, D. Papo, P. A. Sousa, E. Menasalvas + 3 more
The increasing power of computer technology does not dispense with the need to extract meaningful in-formation out of data sets of ever growing size, and indeed typically exacerbates the complexity of this task. To tackle this general problem, two methods have emerged, at chronologically different times, that are now…
Mark M. McAvoy, Lei Liu, Ruiwen Zhou, Benjamin A. Philip
Numerous methods exist to analyze functional MRI (fMRI) data, but no software currently exists to integrate the commonly-used FSL statistical analysis software with alternative preprocessing methods from the Human Connectome Project. Here we developed the Connectome Operations For FSL ExEcution (COFFEE) pipeline to…
Martin A. Lindquist, Stephan Geuter, Tor D. Wager, Brian S. Caffo
The preprocessing pipelines typically used in both task and restingstate fMRI (rs-fMRI) analysis are modular in nature: They are composed of a number of separate filtering/regression steps, including removal of head motion covariates and band-pass filtering, performed sequentially and in a flexible order. In this paper…
Tom M Toner, Paul Miller, Thorsten Forster, Helen G Coleman + 1 more
Integration of data from multiple domains can greatly enhance the quality and applicability of knowledge generated in analysis workflows. However, working with health data is challenging, requiring careful preparation in order to support meaningful interpretation and robust results. Ontologies encapsulate relationships…
Gökmen Altay, Jose Zapardiel-Gonzalo, Bjoern Peters
Gene network inference (GNI) methods have the potential to reveal functional relationships between different genes and their products. Most GNI algorithms have been developed for microarray gene expression datasets and their application to RNA-seq data is relatively recent. As the characteristics of RNA-seq data are…
Nathan P. Golightly, Anna I. Bischoff, Avery Bell, Parker D. Hollingsworth + 1 more
Genome-wide transcriptional profiles provide broad insights into cellular activity. One important use of such data isto identify relationships between transcription levels and patient outcomes. These translational insights can guide the development of biomarkers for predicting outcomes in clinical settings. Over the…
Tarini Naravane, Ilias Tagkopoulos
The future of personalized health relies on knowledge of dietary composition. The current analytical methods are impractical to scale up, and the computational methods are inadequate. We propose machine learning models to predict the nutritional profiles of cooked foods given the raw food composition and cooking…